Analysing the Effect of AI-Driven Performance Management Systems on Employee Motivation and Job Satisfaction: Systematic Literature Review

G. Muridzi () and S. Dhliwayo ()
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G. Muridzi: University of Johannesburg
S. Dhliwayo: University of Johannesburg

A chapter in Embracing Technological Agility in Accounting and Business – Vol. 2, 2026, pp 249-264 from Springer

Abstract: Abstract This study aims to explore how artificial intelligence (AI)-driven performance management systems influence employee motivation and job satisfaction within modern workplaces. As organisations increasingly adopt AI-driven performance management tools, it is crucial to assess their effect on employee motivation, engagement, and overall well-being. This study uses a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to examine the influence of AI-driven performance management systems on employee motivation and job satisfaction within modern workplaces. The study found that AI systems contribute significantly to improving the accuracy and objectivity of performance evaluations, boosting motivation by aligning personal and organisational objectives. The study established that although AI can revolutionise human resource (HR) practices, its effectiveness is greatly influenced by the extent to which it is implemented ethically, transparently, and inclusively. This research makes a valuable contribution to human resource management (HRM), AI ethics, and organisational behaviour by proposing a strategic framework that optimises AI-driven performance management systems, aiming to enhance employee motivation and satisfaction. The findings have wide-ranging implications for businesses, human resource professionals, and policymakers in developing fair, efficient, and ethical AI-based performance management systems that improve employee motivation and job satisfaction.

Keywords: Artificial intelligence; Employee motivation; Job satisfaction; Performance management (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-13384-7_18

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DOI: 10.1007/978-3-032-13384-7_18

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